Legal experts analyze the challenging defense strategy for Tyler Robinson, who allegedly shot Charlie Kirk at Utah Valley University, as prosecutors prepare evidence for trial.

BBC Verify has been to the house in Washington, Utah which has been linked to Tyler Robinson - the suspect in the killing of Charlie Kirk. Sitting in the driveway was a grey car, similar to the model detectives said the suspect had driven to Utah Valley University where Kirk was fatally shot. BBC Verify's Nick Beake has been searching for answers at the location and on social media. Angola: The notorious prison being used in Trump's immigration crackdown The new detention facility inside the prison is designed to hold more than 400 undocumented immigrants convicted of serious crimes. Jackson Denio, a 13-year-old from New Hampshire, might have set the world record for the largest catch of a halibut fish.

In many cases, federal agents can request access to state DMV records by filling out a form. This is an example of a Homeland Security request that was made to the Vermont Department of Motor Vehicles in 2017. In many cases, federal agents can request access to state DMV records by filling out a form. This is an example of a Homeland Security request that was made to the Vermont Department of Motor Vehicles in 2017. Immigration and Customs Enforcement agents mine millions of driver's license photos for possible facial recognition matches -- and some of those efforts target undocumented immigrants who have legally obtained driver's licenses, according to researchers at Georgetown University Law Center, which obtained documents related to the searches.

Now we're going to look more broadly at what's been revealed today about ICE turning to DMV offices for help with facial recognition - that is, using driver's license photographs and algorithms to identify people suspected of being in the country illegally. Now, this collaboration was unearthed by a team at Georgetown University, and here to brief us is NPR's Aarti Shahani. CORNISH: I understand that in the past, ICE has gone to DMV offices and just asked for records on immigrants. We just heard about the case in Vermont that alleges that much. What exactly is new here?

Many convex problems in machine learning and computer science share the same form: \begin{align*} \min_{x} \sum_{i} f_i( A_i x + b_i), \end{align*} where $f_i$ are convex functions on $\mathbb{R}^{n_i}$ with constant $n_i$, $A_i \in \mathbb{R}^{n_i \times d}$, $b_i \in \mathbb{R}^{n_i}$ and $\sum_i n_i = n$. This problem generalizes linear programming and includes many problems in empirical risk minimization. In this paper, we give an algorithm that runs in time \begin{align*} O^* ( ( n^{\omega} + n^{2.5 - \alpha/2} + n^{2+ 1/6} ) \log (n / \delta) ) \end{align*} where $\omega$ is the exponent of matrix multiplication, $\alpha$ is the dual exponent of matrix multiplication, and $\delta$ is the relative accuracy. Note that the runtime has only a log dependence on the condition numbers or other data dependent parameters and these are captured in $\delta$. For the current bound $\omega \sim 2.38$ [Vassilevska Williams'12, Le Gall'14] and $\alpha \sim 0.31$ [Le Gall, Urrutia'18], our runtime $O^* ( n^{\omega} \log (n / \delta))$ matches the current best for solving a dense least squares regression problem, a special case of the problem we consider. Very recently, [Alman'18] proved that all the current known techniques can not give a better $\omega$ below $2.168$ which is larger than our $2+1/6$. Our result generalizes the very recent result of solving linear programs in the current matrix multiplication time [Cohen, Lee, Song'19] to a more broad class of problems. Our algorithm proposes two concepts which are different from [Cohen, Lee, Song'19] : $\bullet$ We give a robust deterministic central path method, whereas the previous one is a stochastic central path which updates weights by a random sparse vector. $\bullet$ We propose an efficient data-structure to maintain the central path of interior point methods even when the weights update vector is dense.